Italian researchers have developed a minimally invasive procedure to treat tendonitis in the rotator cuff of the shoulder that provides immediate symptom relief.
The study, published in the July issue of Radiology, found that ultrasound-guided nonsurgical therapy significantly reduces pain from calcific tendonitis of the rotator cuff and restores lasting mobility after treatment.
Co-author Dr. Luca Sconfienza from the University of Milan said the procedure provides a single inexpensive and effective treatment for calcific tendonitis of the rotator cuff and can replace other treatments that are affected by several limitations and complications.
For the study, senior author Dr. Giovanni Serafini from Santa Corona Hospital in Pietra Ligure and colleagues used ultrasound-guided percutaneous therapy to treat 235 shoulders in 133 women and 86 men with calcific tendonitis.
An additional 68 patients (31 men and 37 women) did not receive treatment and acted as a control group. All of the patients had shoulder pain that was unresponsive to previous medical treatment. Follow-up was conducted after one month, three months, one year, five years, and 10 years.
The results showed that treated patients exhibited a considerable reduction in pain and significant improvement in mobility of the affected limb after one month, three months, and one year, compared to patients who weren't treated.
Five and 10 years after the procedure, the condition of nontreated patients had improved to the point that reported outcomes were similar to those of the treated group.
Related Reading
Noncontrast MRI effective in adhesive capsulitis diagnosis, June 3, 2009
Ultrasound elastography shows strength for diagnosing rotator cuff tears, January 15, 2009
Isotropic 3T MRI aids in diagnosing labrum, rotator cuff tears, December 29, 2008
MR arthrography outdoes 3-tesla MR on tendon tears, December 12, 2008
Making the most of MRI to assess the rotator cuff pre- and postinjury, November 9, 2007
Copyright © 2009 AuntMinnie.com









![Overview of the study design. (A) The fully automated deep learning framework was developed to estimate body composition (BC) (defined as subcutaneous adipose tissue [SAT] in liters; visceral adipose tissue [VAT] in liters; skeletal muscle [SM] in liters; SM fat fraction [SMFF] as a percentage; and intramuscular adipose tissue [IMAT] in deciliters) from MRI. The fully automated framework comprised one model (model 1) to quantify different BC measures (SAT, VAT, SM, SMFF, and IMAT) as three-dimensional (3D) measures from whole-body MRI scans. The second model (model 2) was trained to identify standardized anatomic landmarks along the craniocaudal body axis (z coordinate field), which allowed for subdividing the whole-body measures into different subregions typically examined on clinical routine MRI scans (chest, abdomen, and pelvis). (B) BC was quantified from whole-body MRI in over 66,000 individuals from two large population-based cohort studies, the UK Biobank (UKB) (36,317 individuals) and the German National Cohort (NAKO) (30,291 individuals). Bar graphs show age distribution by sex and cohort. BMI = body mass index. (C) After the performance assessment of the fully automated framework, the change in BC measures, distributions, and profiles across age decades were investigated. Age-, sex-, and height-adjusted body composition reference curves were calculated and made publicly available in a web-based z-score calculator (https://circ-ml.github.io).](https://img.auntminnieeurope.com/mindful/smg/workspaces/default/uploads/2026/05/body-comp.XgAjTfPj1W.jpg?auto=format%2Ccompress&fit=crop&h=112&q=70&w=112)





